Methodological approaches to support process improvement in emergency departments: a systematic review

The most commonly used techniques for addressing each Emergency Department (ED) problem (overcrowding, prolonged waiting time, extended length of stay, excessive patient flow time, and high left-without-being-seen (LWBS) rates) were specified to provide healthcare managers and researchers with a useful framework for effectively solving these operational deficiencies. Finally, we identified the existing research tendencies and highlighted opportunities for future work. We implemented the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to undertake a review including scholarly articles published between April 1993 and October 2019. The selected papers were categorized considering the leading ED problems and publication year. Two hundred and three (203) papers distributed in 120 journals were found to meet the inclusion criteria. Furthermore, computer simulation and lean manufacturing were concluded to be the most prominent approaches for addressing the leading operational problems in EDs. In future interventions, ED administrators and researchers are widely advised to combine Operations Research (OR) methods, quality-based techniques, and data-driven approaches for upgrading the performance of EDs. On a different tack, more interventions are required for tackling overcrowding and high left-without-being-seen rate

An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success

A Machine Learning Framework for Length of Stay Minimization in Healthcare Emergency Department

The emergency departments (EDs) in most hospitals, especially in middle-and-low-income countries, need techniques for minimizing the waiting time of patients. The application and utilization of appropriate methods can enhance the number of patients treated, improve patients’ satisfaction, reduce healthcare costs, and lower morbidity and mortality rates which are often associated with poor healthcare facilities, overcrowding, and low availability of healthcare professionals.  Modeling the length of stay (LOS) of patients in healthcare systems is a challenge that must be addressed for sound decision-making regarding capacity planning and resource allocation. This paper presents a machine learning (ML) framework for predicting a patient’s LOS within the ED. A study of the services in the ED of a tertiary healthcare facility in Uyo, Nigeria was conducted to gain insights into its operational procedures and evaluate the impact of certain parameters on LOS. Then, a computer simulation of the system was performed in R programming language using data obtained from records in the hospital. Finally, the performance of four ML classifiers involved in patients’ LOS prediction: Classification and Regression Tree (CART), Random Forest (RF), K-Nearest Neighbour (K-NN), and Support Vector Machine (SVM), were evaluated and results indicate that SVM outperforms others with the highest coefficient of determination (R2) score of 0.986984 and least mean square error (MSE) value of 0.358594. The result demonstrates the capability of ML techniques to effectively assess the performance of healthcare systems and accurately predict patients’ LOS to mitigate the low physician-patient ratio and improve throughput

Models of Emergency Departments for Reducing Patient Waiting Times

In this paper, we apply both agent-based models and queuing models to investigate patient access and patient flow through emergency departments. The objective of this work is to gain insights into the comparative contributions and limitations of these complementary techniques, in their ability to contribute empirical input into healthcare policy and practice guidelines. The models were developed independently, with a view to compare their suitability to emergency department simulation. The current models implement relatively simple general scenarios, and rely on a combination of simulated and real data to simulate patient flow in a single emergency department or in multiple interacting emergency departments. In addition, several concepts from telecommunications engineering are translated into this modeling context. The framework of multiple-priority queue systems and the genetic programming paradigm of evolutionary machine learning are applied as a means of forecasting patient wait times and as a means of evolving healthcare policy, respectively. The models' utility lies in their ability to provide qualitative insights into the relative sensitivities and impacts of model input parameters, to illuminate scenarios worthy of more complex investigation, and to iteratively validate the models as they continue to be refined and extended. The paper discusses future efforts to refine, extend, and validate the models with more data and real data relative to physical (spatial–topographical) and social inputs (staffing, patient care models, etc.). Real data obtained through proximity location and tracking system technologies is one example discussed

Solving Combinatorial Optimization Problems Using Genetic Algorithms and Ant Colony Optimization

This dissertation presents metaheuristic approaches in the areas of genetic algorithms and ant colony optimization to combinatorial optimization problems. Ant colony optimization for the split delivery vehicle routing problem An Ant Colony Optimization (ACO) based approach is presented to solve the Split Delivery Vehicle Routing Problem (SDVRP). SDVRP is a relaxation of the Capacitated Vehicle Routing Problem (CVRP) wherein a customer can be visited by more than one vehicle. The proposed ACO based algorithm is tested on benchmark problems previously published in the literature. The results indicate that the ACO based approach is competitive in both solution quality and solution time. In some instances, the ACO method achieves the best known results to date for the benchmark problems. Hybrid genetic algorithm for the split delivery vehicle routing problem (SDVRP) The Vehicle Routing Problem (VRP) is a combinatory optimization problem in the field of transportation and logistics. There are various variants of VRP which have been developed of the years; one of which is the Split Delivery Vehicle Routing Problem (SDVRP). The SDVRP allows customers to be assigned to multiple routes. A hybrid genetic algorithm comprising a combination of ant colony optimization, genetic algorithm, and heuristics is proposed and tested on benchmark SDVRP test problems. Genetic algorithm approach to solve the hospital physician scheduling problem Emergency departments have repeating 24-hour cycles of non-stationary Poisson arrivals and high levels of service time variation. The problem is to find a shift schedule that considers queuing effects and minimizes average patient waiting time and maximizes physicians’ shift preference subject to constraints on shift start times, shift durations and total physician hours available per day. An approach that utilizes a genetic algorithm and discrete event simulation to solve the physician scheduling problem in a hospital is proposed. The approach is tested on real world datasets for physician schedules

Process improvement approaches for increasing the response of emergency departments against the Covid-19 pandemic: a systematic review

The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions

Redesigning the Barranquilla's public emergency care network to improve the patient waiting time

Tesis por compendio[ES] La oportunidad en la atención es uno de los críticos de mayor relevancia en la satisfacción de los pacientes que acuden a los servicios de Urgencias. Por tal motivo, las instituciones prestadoras de servicio y las organizaciones gubernamentales deben propender conjuntamente por una atención cada vez más oportuna a costos operacionales razonables. En el caso de la Red Pública en Servicios de Urgencias de Barrannquilla, compuesta por 8 puntos de atención y 2 hospitales, la tendencia marca un continuo crecimiento de la oportunidad en la atención con una tasa de 3,08 minutos/semestre y una probabilidad del 93,13% de atender a los pacientes después de una espera mayor a 30 minutos. Lo anterior se constituye en un síntoma inequívoco de la incapacidad de la Red para satisfacer los estándares de oportunidad establecidos por el Ministerio de Salud, hecho que podría desencadenar el desarrollo de sintomatologías de mayor complejidad, el incremento de la probabilidad de mortalidad, el requerimiento de servicios clínicos más complejos (hospitalización y cuidados intensivos) y el aumento de los costos asociados al servicio. En consecuencia, la presente tesis doctoral presenta el rediseño de la Red Pública en Servicios de Urgencias anteriormente mencionada a fin de otorgar a la población diana un servicio eficiente y altamente oportuno donde tanto las instituciones prestadoras del servicio como los organismos gubernamentales converjan efectivamente. Para ello, fue necesaria la ejecución de 4 grandes fases a través de las cuales se consolidó una propuesta orientada al desarrollo efectivo y sostenible de las operaciones de la Red. Primero, se caracterizó la Red Pública de Servicios de Urgencias en Salud considerando su comportamiento actual en términos de demanda y oportunidad de la atención. Luego, a través de una revisión sistemática de la literatura, se identificaron los enfoques metodológicos que se han implementado para la mejora de la oportunidad y otros indicadores de rendimiento asociados al servicio de Urgencias. Posteriormente, se diseñó una metodología para la creación de redes de Urgencias eficientes y sostenibles la cual luego se validó en la Red Pública sudamericana a fin de disminuir la oportunidad de atención promedio en Urgencias y garantizar la distribución equitativa de los beneficios financieros derivados de la colaboración. Finalmente, se construyó un modelo multicriterio que permitió evaluar el rendimiento de los departamentos de Urgencia e impulsó la creación de estrategias de mejora focalizadas en incrementar su respuesta ante la demanda cambiante, los críticos de satisfacción y las condiciones de operación estipuladas en la ley. Los resultados de esta aplicación evidenciaron que los pacientes que acceden a la Red tienden a esperar en promedio 201,6 min con desviación de estándar de 81,6 min antes de ser atendidos por urgencia. Por otro lado, de acuerdo con la revisión de literatura, la combinación de técnicas de investigación de operaciones, ingeniería de la calidad y analítica de datos es ampliamente recomendada para abordar este problema. En ese sentido, una metodología basada en modelos colaterales de pago, simulación de procesos y lean seis sigma fue propuesta y validada generando un rediseño de Red cuya oportunidad de atención promedio podría disminuir entre 6,71 min y 9,08 min con beneficios financieros promedio de US$29,980/nodo. En último lugar, un modelo compuesto por 8 criterios y 35 sub-criterios fue diseñado para evaluar el rendimiento general de los departamentos de Urgencias. Los resultados del modelo evidenciaron el rol crítico de la infraestructura (Peso global = 21,5i garantir la distribució equitativa dels beneficis financers derivats de la col´laboració. Finalment, es va construir un model multicriteri que va permetre avaluar el rendiment dels departaments d'Urgència i va impulsar la creació d'estratègies de millora focalitzades en incrementar la seva resposta davant la demanda canviant, els crítics de satisfacció i les condicions d'operació estipulades en la llei. Els resultats d'aquesta aplicació van evidenciar que els pacients que accedeixen a la Xarxa tendeixen a esperar de mitjana 201,6 min amb desviació d'estàndard de 81,6 min abans de ser atesos per urgència. D'altra banda, d'acord amb la revisió de literatura, la combinació de tècniques d'investigació d'operacions, enginyeria de la qualitat i analítica de dades és àmpliament recomanada per abordar aquest problema. En aquest sentit, una metodologia basada en models col´laterals de pagament, simulació de processos i llegeixin 6 sigma va ser proposada i validada generant un redisseny de Xarxa la oportunitat d'atenció mitjana podria disminuir entre 6,71 min i 9,08 min amb beneficis financers mitjana d'US$ 29,980 / node. En darrer lloc, un model compost per 8 criteris i 35 sub-criteris va ser dissenyat per avaluar el rendiment general dels departaments d'Urgències. Els resultats de el model evidenciar el paper crític de la infraestructura (Pes global = 21,5%) en el rendiment dels departaments d'Urgència i la naturalesa interactiva de la Seguretat de l'Pacient (C + R = 12,771).[EN] Waiting time is one of the most critical measures in the satisfaction of patients admitted within emergency departments. Therefore, hospitals and governmental organizations should jointly aim to provide timely attention at reasonable costs. In the case of Barranquilla's Pubic Emergency Service Network, composed by 8 Points of care (POCs) and 2 hospitals, the trend evidences a continuous growing of the waiting time with a rate of 3,08 min/semester and a 93,13% likelihood of serving patients after waiting for more than 30 minutes. This is an unmistakable symptom of the network inability for satisfying the standards established by the Ministry of Health, which may trigger the development of more complex symptoms, increase in the death rate, requirement for more complex clinical services (hospitalization and intensive care unit) and increased service costs. This doctoral dissertation then illustrates the redesign of the aforementioned Public Emergency Service Network aiming at providing the target population with an efficient and highly timely service where both hospitals and governmental institutions effectively converge. It was then necessary to implement a 4-phase methodology consolidating a proposal oriented to the effective and sustainable development of network operations. First, the Public Emergency Service Network was characterized considering its current behavior in terms of demand and waiting time. A systematic literature review was then undertaken for identifying the methodological approaches that have been implementing for improving the waiting time and other performance indicators associated with the emergency care service. Following this, a methodology for the creation of efficient and sustainable emergency care networks was designed and later validated in the Southamerican Public network for lessening the average waiting time and ensuring the equitable distribution of profits derived from the collaboration. Ultimately, a multicriteria decision-making model was created for assessing the performance of the emergency departments and propelling the design of improvement strategies focused on bettering the response against the changing demand conditions, critical to satisfaction and operational conditions. The results evidenced that the patients accessing to the network tend to wait 201,6 min on average with a standard deviation of 81,6 min before being served by the emergency care unit. On the other hand, based on the reported literature, it is highly suggested to combine Operations Research (OR) methods, quality-based techniques, and data-driven approaches for addressing this problem. In this sense, a methodology based on collateral payment models, Discrete-event simulation, and Lean Six Sigma was proposed and validated resulting in a redesigned network whose average waiting time may diminish between 6,71 min and 9,08 min with an average profit US$29,980/node. Lately, a model comprising of 8 criteria and 35 sub-criteria was designed for evaluating the overall performance of emergency departments. The model outcomes revealed the critical role of Infrastructure (Global weight = 21,5%) in ED performance and the interactive nature of Patient Safety (C + R = 12,771).Ortíz Barrios, MÁ. (2020). Redesigning the Barranquilla's public emergency care network to improve the patient waiting time [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/156215TESISCompendi

Modelling human network behaviour using simulation and optimization tools: the need for hybridization

The inclusion of stakeholder behaviour in Operations Research / Industrial Engineering (OR/IE) models has gained much attention in recent years. Behavioural and cognitive traits of people and groups have been integrated in simulation models (mainly through agent-based approaches) as well as in optimization algorithms. However, especially the influence of relations between different actors in human networks is a broad and interdisciplinary topic that has not yet been fully investigated. This paper analyses, from an OR/IE point of view, the existing literature on behaviour-related factors in human networks. This review covers different application fields, including: supply chain management, public policies in emergency situations, and Internet-based human networks. The review reveals that the methodological approach of choice (either simulation or optimization) is highly dependent on the application area. However, an integrated approach combining simulation and optimization is rarely used. Thus, the paper proposes the hybridization of simulation with optimization as one of the best strategies to incorporate human behaviour in human networks and the resulting uncertainty, randomness, and dynamism in related OR/IE models.Peer Reviewe

Agent based simulation to optimise emergency departments

Nowadays, many of the health care systems are large and complex environments and quite dynamic, specifically Emergency Departments, EDs. It is opened and working 24 hours per day throughout the year with limited resources, whereas it is overcrowded. Thus, is mandatory to simulate EDs to improve qualitatively and quantitatively their performance. This improvement can be achieved modelling and simulating EDs using Agent-Based Model, ABM and optimising many different staff scenarios. This work optimises the staff configuration of an ED. In order to do optimisation, objective functions to minimise or maximise have to be set. One of those objective functions is to find the best or optimum staff configuration that minimise patient waiting time. The staff configuration comprises: doctors, triage nurses, and admissions, the amount and sort of them. Staff configuration is a combinatorial problem, that can take a lot of time to be solved. HPC is used to run the experiments, and encouraging results were obtained. However, even with the basic ED used in this work the search space is very large, thus, when the problem size increases, it is going to need more resources of processing in order to obtain results in an acceptable time